Multivariate analysis (MVA) has proven to be an effective tool in process monitoring involving a large number of monitored parameters, and in particular for fault detection. Multivariate analysis detects process changes seen in parameter covariance and correlation. MVA typically requires the construction of a process reference model based on known acceptable working conditions that serve as a reference.
The reference model can be constructed from measured process parameters. In general, the model can be broken down into individual time-dependent models for the individual process steps, and then reconstructed into an upper level model summarizing the overall process.
All subsequent implementations of fault detection for the process compare the same measured parameters in the same fashion (i.e., modeling process steps and summarizing into an upper level model) and determine statistically significant deviations from the known acceptable processes upon which the reference model is based. Effective implementation of MVA depends in large part on the quality of the model.